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In science, engineering, and research, expert elicitation is the synthesis of opinions of authorities of a subject where there is uncertainty due to insufficient data or when such data is unattainable because of physical constraints or lack of resources.[1] Expert elicitation is essentially a scientific consensus methodology. It is often used in the study of rare events.[2] Expert elicitation allows for parametrization, an "educated guess", for the respective topic under study. Expert elicitation generally quantifies uncertainty.

Expert elicitation tends to be multidisciplinary as well as interdisciplinary, with practically universal applicability, and is used in a broad range of fields. Prominent recent expert elicitation applications include climate change, modeling seismic hazard and damage, association of tornado damage to wind speed in developing the Enhanced Fujita scale, risk analysis for nuclear waste storage.

In performing expert elicitation certain factors need to be taken into consideration. The topic must be one for which there are people who have predictive expertise. Furthermore, the objective should be to obtain an experts' carefully considered judgment based on a systematic consideration of all relevant evidence. For this reason one should take care to adopt strategies designed to help the expert being interviewed to avoid overlooking relevant evidence. Additionally, vocabulary used should face intense scrutiny; qualitative uncertainty words such as "likely" and "unlikely" are not sufficient and can lead to confusion. Such words can mean very different things to different people, or to the same people in different situations.[3]

See also

References

  1. ^ van der Sluijs, Jeroen P.; et al. (2008). "Expert Elicitation: Methodological suggestions for its use in environmental health impact assessments" (PDF). NUSAP. Retrieved 25 November 2015.
  2. ^ Schwarzenegger, Rafael; Quigley, John; Walls, Lesley (23 November 2021). "Is eliciting dependency worth the effort? A study for the multivariate Poisson-Gamma probability model". Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability: 1748006X2110594. doi:10.1177/1748006X211059417. S2CID 244549831.
  3. ^ Tversky, Amos; Kahneman, Daniel (27 September 1974). Judgments under uncertainty: Heuristics and biases (PDF) (Vol. 185, No. 4157 ed.). Science. pp. 1124–1131. Archived from the original (PDF) on 28 May 2019. Retrieved 25 November 2015.

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